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    "## 一、赛题描述\n",
    "\n",
    "随着城市化步伐的加速迈进，城市治理面临着前所未有的挑战与机遇。城市管理的精细化、智能化已成为全球城市追求卓越的关键路径。然而，机动车违停、非机动车违停、占道经营等城市违规行为如同现代都市肌体上的疮疤，不仅侵蚀着城市的美学与秩序，更对公众福祉构成了潜在威胁。传统的人力巡查与被动响应模式，已然无法匹配当今城市治理的需求。\n",
    "\n",
    "本赛题最终目标是开发一套智能识别系统，能够自动检测和分类城市管理中的违规行为。该系统应利用先进的图像处理和计算机视觉技术，通过对摄像头捕获的视频进行分析，自动准确识别违规行为，并及时向管理部门发出告警，以实现更高效的城市管理。\n",
    "\n",
    "## 二、赛题题目\n",
    "\n",
    "城市管理违规行为智能识别\n",
    "\n",
    "## 三、赛题任务\n",
    "\n",
    "- 【初赛】\n",
    "\n",
    "初赛任务是根据给定的城管视频监控数据集，进行城市违规行为的检测。违规行为主要包括垃圾桶满溢、机动车违停、非机动车违停等。\n",
    "\n",
    "选手需要能够从视频中分析并标记出违规行为，提供违规行为发生的时间和位置信息。\n",
    "\n",
    "- 【初赛审核】\n",
    "\n",
    "阶段时间：9月13日-9月19日 前24名晋级复赛，如出现违规行为，则顺延晋级，由组委会确认晋级名单。\n",
    "\n",
    "- 【复赛】\n",
    "\n",
    "复赛任务是根据给定的城管视频监控数据集，进行城市违规行为的检测。违规行为主要包括违法经营、垃圾桶满溢、机动车违停、非机动车违停等。\n",
    "\n",
    "选手需要基于大赛提供的复赛环境，进行复赛模型构建与预测，同时复赛数据集对选手不可见。\n",
    "\n",
    "- 【复赛审核】\n",
    "\n",
    "阶段时间：9月18日-9月30日 前12名晋级决赛，如出现违规行为，则顺延晋级，由组委会确认晋级名单。\n",
    "\n",
    "- 【决赛】\n",
    "\n",
    "具体时间待定，决赛阶段采用线下或者线上答辩的方式（待定），晋级决赛队伍需要提前准备答辩PPT及相关支撑材料，评委将根据选手的初复赛及答辩表现进行综合评分，决定最终排名。\n",
    "\n",
    "## 四、数据描述\n",
    "\n",
    "- **【初赛】**\n",
    "\n",
    "初赛提供城管视频监控数据与对应违规行为标注。违规行为包括垃圾桶满溢、机动车违停、非机动车违停等。\n",
    "\n",
    "视频数据为mp4格式，标注文件为json格式，每个视频对应一个json文件。\n",
    "\n",
    "json文件的内容是每帧检测到的违规行为，包括以下字段：\n",
    "\n",
    "- frame_id：违规行为出现的帧编号\n",
    "- event_id：违规行为ID\n",
    "- category：违规行为类别\n",
    "- bbox：检测到的违规行为矩形框的坐标，[xmin,ymin,xmax,ymax]形式\n",
    "\n",
    "标注示例如下：\n",
    "\n",
    "```\n",
    "[\n",
    "  {\n",
    "   \"frame_id\": 20,\n",
    "   \"event_id\": 1,\n",
    "   \"category\": \"机动车违停\",\n",
    "   \"bbox\": [200, 300, 280, 400]\n",
    "  },\n",
    "  {\n",
    "   \"frame_id\": 20,\n",
    "   \"event_id\": 2,\n",
    "   \"category\": \"机动车违停\",\n",
    "   \"bbox\": [600, 500, 720, 560]\n",
    "  },\n",
    "  {\n",
    "   \"frame_id\": 30,\n",
    "   \"event_id\": 3,\n",
    "   \"category\": \"垃圾桶满溢\",\n",
    "   \"bbox\": [400, 500, 600, 660]\n",
    "  }\n",
    " ]\n",
    "```\n",
    "\n",
    "\n",
    "违规行为示例如下：\n",
    "\n",
    "\n",
    "![img](https://file.public.marsbigdata.com/2024/08/14/f0gScKCpVXIJCRBJ.png)\n",
    "\n",
    "垃圾桶满溢\n",
    "\n",
    "![img](https://file.public.marsbigdata.com/2024/08/14/6ihe6W8nDp_ZqUNX.png)\n",
    "\n",
    "机动车违停\n",
    "\n",
    "![img](https://file.public.marsbigdata.com/2024/08/14/1kkQ9vrT8bZTEBVN.png)\n",
    "\n",
    "非机动车违停\n",
    "\n",
    "- **【复赛】**\n",
    "\n",
    "复赛提供城管视频监控数据与对应违规行为标注。违规行为包括违法经营、垃圾桶满溢、机动车违停、非机动车违停等。\n",
    "\n",
    "数据与初赛数据格式相同，新增违法经营类别，行为示例如下：\n",
    "\n",
    "![img](https://file.public.marsbigdata.com/2024/08/14/rksAIZHbVMj-3PAg.png)\n",
    "\n",
    "违法经营\n",
    "\n",
    "\n",
    "## 五、评估指标\n",
    "\n",
    "- **【初赛】**\n",
    "\n",
    "使用F1score、MOTA指标来评估模型预测结果。\n",
    "\n",
    "\n",
    "![img](https://file.public.marsbigdata.com/2024/08/14/1-2m0dPNpL-IX5au.png)\n",
    "\n",
    "对每个json文件得到两个指标的加权求和，最终得分为所有文件得分取均值。\n",
    "\n",
    "注1：若真实目标框与预测框IOU大于0.5，则判定目标正确识别。若MOTA指标为负，则该类别精度得分为0。\n",
    "\n",
    "注2：若该视频中没有某个类别的目标，则此类别计算均值时，忽略该视频。\n",
    "\n",
    "- **【****复****赛】**\n",
    "\n",
    "复赛需同时评估模型的准确度与效率。\n",
    "\n",
    "模型准确率评估指标与初赛一致，使用F1score、MOTA进行评估。\n",
    "\n",
    "模型效率使用FPS（每秒钟能够处理的帧数）等进行评估。\n",
    "\n",
    "## 六、提交说明\n",
    "\n",
    "- 【初赛】**\n",
    "\n",
    "选手需要生成result文件夹，文件夹中包含每个视频对应的json结果文件，文件名与视频名对应。选手需要将文件夹打包成result.zip进行上传。\n",
    "\n",
    "json文件中包含了检测到的违规行为列表，若未检测到违规行为，则列表为空。\n",
    "\n",
    "每个违规行为包含的字段如下：\n",
    "\n",
    "- frame_id：违规行为出现的帧编号\n",
    "- event_id：违规行为ID\n",
    "- category：违规行为类别\n",
    "- bbox：检测到的违规行为矩形框的坐标，[xmin,ymin,xmax,ymax]形式\n",
    "- confidence：置信度\n",
    "\n",
    "提交的json示例如下：\n",
    "\n",
    "```\n",
    "[\n",
    "  {\n",
    "   \"frame_id\": 20,\n",
    "   \"event_id\": 1,\n",
    "   \"category\": \"机动车违停\",\n",
    "   \"bbox\": [200, 300, 280, 500],\n",
    "   \"confidence\": 0.85\n",
    "  },\n",
    "  {\n",
    "   \"frame_id\": 20,\n",
    "   \"event_id\": 2,\n",
    "   \"category\": \"垃圾桶满溢\",\n",
    "   \"bbox\": [600, 500,720, 560],\n",
    "   \"confidence\": 0.90\n",
    "  },\n",
    "  {\n",
    "   \"frame_id\": 30,\n",
    "   \"event_id\": 3,\n",
    "   \"category\": \"垃圾桶满溢\",\n",
    "   \"bbox\": [400, 500, 500, 560],\n",
    "   \"confidence\": 0.78\n",
    "  }\n",
    " ]\n",
    "```\n",
    "\n",
    "\n",
    "**注：赛题禁止对测试集数据进行人工标注，用于训练或结果提交。初赛审核阶段，会对此类情况严格审核，一经发现，即取消复赛晋级资格。**\n",
    "\n",
    "- **【复赛】**\n",
    "\n",
    "复赛与初赛的提交内容一致。\n",
    "\n",
    "注：**复赛阶段的训练集与测试集对选手不可见**，选手在模型**开发调试阶段**时使用的数据是一部分样例数据，提交代码后，后台系统将自动使用复赛全量数据进行复现评测。\n",
    "\n",
    "## 七、违法标准\n",
    "\n",
    "【机动车违停】\n",
    "\n",
    "机动车在设有禁止停车标志、标线的路段停车，或在非机动车道、人行横道、施工地段等禁止停车的地方停车。具体包含以下：\n",
    "\n",
    "1、无论有无禁停标志，机动车道禁止车辆停放；\n",
    "\n",
    "2、距路口、桥梁50米以内禁止车辆停放；\n",
    "\n",
    "3、距公交车站、消防栓、消防队、医院30米以内禁止使用上述设施以外的车辆停放；\n",
    "\n",
    "4、禁止车辆双排停放、靠左侧停放、横向停放、逆向停放；\n",
    "\n",
    "5、人行道仅允许在已设置的停车泊位内停车，禁止在停车泊位外停车；\n",
    "\n",
    "6、在设有禁停标志、标线的路段，人行横道、施工路段，不得停车。\n",
    "\n",
    "【非机动车违停】\n",
    "\n",
    "非机动车（如自行车、‌电动车等）‌未按照规定停放在指定的非机动车停车泊位或停车线内，‌而是在非机动车禁停区域或未划定的区域（消防通道、盲道、非机动车停车区线外、机动车停车区等）随意停放。\n",
    "\n",
    "【垃圾满溢】\n",
    "\n",
    "生活垃圾收集容器内垃圾超过三分之二以上即为满溢。**‌**垃圾桶无法封闭、脏污且周边有纸屑、污渍、塑料、生活垃圾及杂物堆放。\n",
    "\n",
    "【占道经营】\n",
    "\n",
    "经营者占用城市道路、桥梁、城市广场等公共场所进行盈利性买卖商品或服务的行为。"
   ]
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   "id": "41c9907a-c3ef-43df-8dec-edf7a7f4ae7c",
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      "Collecting scipy>=1.4.1 (from ultralytics)\n",
      "  Downloading https://mirrors.bfsu.edu.cn/pypi/web/packages/e2/20/15c8fe0dfebb6facd81b3d08bf45dfa080e305deb17172b0a40eba59e927/scipy-1.14.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (41.1 MB)\n",
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      "\u001b[?25hRequirement already satisfied: torch>=1.8.0 in /opt/miniconda/lib/python3.10/site-packages (from ultralytics) (2.2.2+cu118)\n",
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      "Requirement already satisfied: psutil in /opt/miniconda/lib/python3.10/site-packages (from ultralytics) (6.0.0)\n",
      "Collecting py-cpuinfo (from ultralytics)\n",
      "  Downloading https://mirrors.bfsu.edu.cn/pypi/web/packages/e0/a9/023730ba63db1e494a271cb018dcd361bd2c917ba7004c3e49d5daf795a2/py_cpuinfo-9.0.0-py3-none-any.whl (22 kB)\n",
      "Collecting seaborn>=0.11.0 (from ultralytics)\n",
      "  Downloading https://mirrors.bfsu.edu.cn/pypi/web/packages/83/11/00d3c3dfc25ad54e731d91449895a79e4bf2384dc3ac01809010ba88f6d5/seaborn-0.13.2-py3-none-any.whl (294 kB)\n",
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      "\u001b[?25hCollecting ultralytics-thop>=2.0.0 (from ultralytics)\n",
      "  Downloading https://mirrors.bfsu.edu.cn/pypi/web/packages/8f/34/08f80cc72ece5ecac4a910b6a572d8bef46d4d9dc37be2a4c8425e6cae75/ultralytics_thop-2.0.5-py3-none-any.whl (25 kB)\n",
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      "Requirement already satisfied: nvidia-cuda-nvrtc-cu11==11.8.89 in /opt/miniconda/lib/python3.10/site-packages (from torch>=1.8.0->ultralytics) (11.8.89)\n",
      "Requirement already satisfied: nvidia-cuda-runtime-cu11==11.8.89 in /opt/miniconda/lib/python3.10/site-packages (from torch>=1.8.0->ultralytics) (11.8.89)\n",
      "Requirement already satisfied: nvidia-cuda-cupti-cu11==11.8.87 in /opt/miniconda/lib/python3.10/site-packages (from torch>=1.8.0->ultralytics) (11.8.87)\n",
      "Requirement already satisfied: nvidia-cudnn-cu11==8.7.0.84 in /opt/miniconda/lib/python3.10/site-packages (from torch>=1.8.0->ultralytics) (8.7.0.84)\n",
      "Requirement already satisfied: nvidia-cublas-cu11==11.11.3.6 in /opt/miniconda/lib/python3.10/site-packages (from torch>=1.8.0->ultralytics) (11.11.3.6)\n",
      "Requirement already satisfied: nvidia-cufft-cu11==10.9.0.58 in /opt/miniconda/lib/python3.10/site-packages (from torch>=1.8.0->ultralytics) (10.9.0.58)\n",
      "Requirement already satisfied: nvidia-curand-cu11==10.3.0.86 in /opt/miniconda/lib/python3.10/site-packages (from torch>=1.8.0->ultralytics) (10.3.0.86)\n",
      "Requirement already satisfied: nvidia-cusolver-cu11==11.4.1.48 in /opt/miniconda/lib/python3.10/site-packages (from torch>=1.8.0->ultralytics) (11.4.1.48)\n",
      "Requirement already satisfied: nvidia-cusparse-cu11==11.7.5.86 in /opt/miniconda/lib/python3.10/site-packages (from torch>=1.8.0->ultralytics) (11.7.5.86)\n",
      "Requirement already satisfied: nvidia-nccl-cu11==2.19.3 in /opt/miniconda/lib/python3.10/site-packages (from torch>=1.8.0->ultralytics) (2.19.3)\n",
      "Requirement already satisfied: nvidia-nvtx-cu11==11.8.86 in /opt/miniconda/lib/python3.10/site-packages (from torch>=1.8.0->ultralytics) (11.8.86)\n",
      "Requirement already satisfied: triton==2.2.0 in /opt/miniconda/lib/python3.10/site-packages (from torch>=1.8.0->ultralytics) (2.2.0)\n",
      "Requirement already satisfied: MarkupSafe>=2.0 in /opt/miniconda/lib/python3.10/site-packages (from jinja2->torch>=1.8.0->ultralytics) (2.1.5)\n",
      "Requirement already satisfied: mpmath>=0.19 in /opt/miniconda/lib/python3.10/site-packages (from sympy->torch>=1.8.0->ultralytics) (1.3.0)\n",
      "Installing collected packages: pytz, py-cpuinfo, tzdata, scipy, pyparsing, opencv-python, kiwisolver, fonttools, cycler, contourpy, pandas, matplotlib, ultralytics-thop, seaborn, ultralytics\n",
      "Successfully installed contourpy-1.2.1 cycler-0.12.1 fonttools-4.53.1 kiwisolver-1.4.5 matplotlib-3.9.2 opencv-python-4.10.0.84 pandas-2.2.2 py-cpuinfo-9.0.0 pyparsing-3.1.2 pytz-2024.1 scipy-1.14.0 seaborn-0.13.2 tzdata-2024.1 ultralytics-8.2.79 ultralytics-thop-2.0.5\n",
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
      "\u001b[0m"
     ]
    }
   ],
   "source": [
    "!/opt/miniconda/bin/pip install opencv-python pandas matplotlib ultralytics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "773bee32-9ba4-4930-8bc9-0ed752f37f8e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os, sys\n",
    "import cv2, glob, json\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "80174d2c-3d3a-4aac-af26-1caa3fe41518",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading package lists... Done\n",
      "Building dependency tree... Done\n",
      "Reading state information... Done\n",
      "The following NEW packages will be installed:\n",
      "  unzip zip\n",
      "0 upgraded, 2 newly installed, 0 to remove and 2 not upgraded.\n",
      "Need to get 350 kB of archives.\n",
      "After this operation, 930 kB of additional disk space will be used.\n",
      "Get:1 http://nova.clouds.archive.ubuntu.com/ubuntu jammy-updates/main amd64 unzip amd64 6.0-26ubuntu3.2 [175 kB]\n",
      "Get:2 http://nova.clouds.archive.ubuntu.com/ubuntu jammy/main amd64 zip amd64 3.0-12build2 [176 kB]\n",
      "Fetched 350 kB in 4s (90.7 kB/s)mm\u001b[33m\n",
      "\n",
      "\u001b7\u001b[0;23r\u001b8\u001b[1ASelecting previously unselected package unzip.\n",
      "(Reading database ... 76345 files and directories currently installed.)\n",
      "Preparing to unpack .../unzip_6.0-26ubuntu3.2_amd64.deb ...\n",
      "\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [  0%]\u001b[49m\u001b[39m [..........................................................] \u001b8\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 11%]\u001b[49m\u001b[39m [######....................................................] \u001b8Unpacking unzip (6.0-26ubuntu3.2) ...\n",
      "\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 22%]\u001b[49m\u001b[39m [############..............................................] \u001b8Selecting previously unselected package zip.\n",
      "Preparing to unpack .../zip_3.0-12build2_amd64.deb ...\n",
      "\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 33%]\u001b[49m\u001b[39m [###################.......................................] \u001b8Unpacking zip (3.0-12build2) ...\n",
      "\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 44%]\u001b[49m\u001b[39m [#########################.................................] \u001b8Setting up unzip (6.0-26ubuntu3.2) ...\n",
      "\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 56%]\u001b[49m\u001b[39m [################################..........................] \u001b8\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 67%]\u001b[49m\u001b[39m [######################################....................] \u001b8Setting up zip (3.0-12build2) ...\n",
      "\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 78%]\u001b[49m\u001b[39m [#############################################.............] \u001b8\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 89%]\u001b[49m\u001b[39m [###################################################.......] \u001b8Processing triggers for man-db (2.10.2-1) ...\n",
      "\n",
      "Scanning processes...                                                                            ]\n",
      "Scanning linux images...                                                        \n",
      "\n",
      "Running kernel seems to be up-to-date.\n",
      "\n",
      "No services need to be restarted.\n",
      "\n",
      "No containers need to be restarted.\n",
      "\n",
      "No user sessions are running outdated binaries.\n",
      "\n",
      "No VM guests are running outdated hypervisor (qemu) binaries on this host.\n",
      "Reading package lists... Done\n",
      "Building dependency tree... Done\n",
      "Reading state information... Done\n",
      "The following additional packages will be installed:\n",
      "  gnustep-base-common gnustep-base-runtime gnustep-common libavahi-client3\n",
      "  libavahi-common-data libavahi-common3 libgc1 libgnustep-base1.28 libobjc4\n",
      "  libwavpack1\n",
      "The following NEW packages will be installed:\n",
      "  gnustep-base-common gnustep-base-runtime gnustep-common libavahi-client3\n",
      "  libavahi-common-data libavahi-common3 libgc1 libgnustep-base1.28 libobjc4\n",
      "  libwavpack1 unar\n",
      "0 upgraded, 11 newly installed, 0 to remove and 2 not upgraded.\n",
      "Need to get 3594 kB of archives.\n",
      "After this operation, 16.1 MB of additional disk space will be used.\n",
      "Get:1 http://nova.clouds.archive.ubuntu.com/ubuntu jammy/universe amd64 gnustep-common amd64 2.9.0-3 [39.0 kB]\n",
      "Get:2 http://nova.clouds.archive.ubuntu.com/ubuntu jammy/universe amd64 gnustep-base-common all 1.28.0-4build1 [81.2 kB]\n",
      "Get:3 http://nova.clouds.archive.ubuntu.com/ubuntu jammy-updates/main amd64 libavahi-common-data amd64 0.8-5ubuntu5.2 [23.8 kB]\n",
      "Get:4 http://nova.clouds.archive.ubuntu.com/ubuntu jammy-updates/main amd64 libavahi-common3 amd64 0.8-5ubuntu5.2 [23.9 kB]\n",
      "Get:5 http://nova.clouds.archive.ubuntu.com/ubuntu jammy-updates/main amd64 libavahi-client3 amd64 0.8-5ubuntu5.2 [28.0 kB]\n",
      "Get:6 http://nova.clouds.archive.ubuntu.com/ubuntu jammy/main amd64 libgc1 amd64 1:8.0.6-1.1build1 [96.8 kB]\n",
      "Get:7 http://nova.clouds.archive.ubuntu.com/ubuntu jammy-updates/universe amd64 libobjc4 amd64 12.3.0-1ubuntu1~22.04 [48.6 kB]\n",
      "Get:8 http://nova.clouds.archive.ubuntu.com/ubuntu jammy/universe amd64 libgnustep-base1.28 amd64 1.28.0-4build1 [1533 kB]\n",
      "Get:9 http://nova.clouds.archive.ubuntu.com/ubuntu jammy/universe amd64 gnustep-base-runtime amd64 1.28.0-4build1 [239 kB]\n",
      "Get:10 http://nova.clouds.archive.ubuntu.com/ubuntu jammy/main amd64 libwavpack1 amd64 5.4.0-1build2 [83.7 kB]\n",
      "Get:11 http://nova.clouds.archive.ubuntu.com/ubuntu jammy/universe amd64 unar amd64 1.10.1-2build11 [1397 kB]\n",
      "Fetched 3594 kB in 4s (815 kB/s)\u001b[0mm\u001b[33m\n",
      "\n",
      "\u001b7\u001b[0;23r\u001b8\u001b[1ASelecting previously unselected package gnustep-common.\n",
      "(Reading database ... 76377 files and directories currently installed.)\n",
      "Preparing to unpack .../00-gnustep-common_2.9.0-3_amd64.deb ...\n",
      "\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [  0%]\u001b[49m\u001b[39m [..........................................................] \u001b8\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [  2%]\u001b[49m\u001b[39m [#.........................................................] \u001b8Unpacking gnustep-common (2.9.0-3) ...\n",
      "\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [  4%]\u001b[49m\u001b[39m [##........................................................] \u001b8Selecting previously unselected package gnustep-base-common.\n",
      "Preparing to unpack .../01-gnustep-base-common_1.28.0-4build1_all.deb ...\n",
      "\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [  7%]\u001b[49m\u001b[39m [###.......................................................] \u001b8Unpacking gnustep-base-common (1.28.0-4build1) ...\n",
      "\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [  9%]\u001b[49m\u001b[39m [#####.....................................................] \u001b8Selecting previously unselected package libavahi-common-data:amd64.\n",
      "Preparing to unpack .../02-libavahi-common-data_0.8-5ubuntu5.2_amd64.deb ...\n",
      "\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 11%]\u001b[49m\u001b[39m [######....................................................] \u001b8Unpacking libavahi-common-data:amd64 (0.8-5ubuntu5.2) ...\n",
      "\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 13%]\u001b[49m\u001b[39m [#######...................................................] \u001b8Selecting previously unselected package libavahi-common3:amd64.\n",
      "Preparing to unpack .../03-libavahi-common3_0.8-5ubuntu5.2_amd64.deb ...\n",
      "\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 16%]\u001b[49m\u001b[39m [#########.................................................] \u001b8Unpacking libavahi-common3:amd64 (0.8-5ubuntu5.2) ...\n",
      "\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 18%]\u001b[49m\u001b[39m [##########................................................] \u001b8Selecting previously unselected package libavahi-client3:amd64.\n",
      "Preparing to unpack .../04-libavahi-client3_0.8-5ubuntu5.2_amd64.deb ...\n",
      "\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 20%]\u001b[49m\u001b[39m [###########...............................................] \u001b8Unpacking libavahi-client3:amd64 (0.8-5ubuntu5.2) ...\n",
      "\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 22%]\u001b[49m\u001b[39m [############..............................................] \u001b8Selecting previously unselected package libgc1:amd64.\n",
      "Preparing to unpack .../05-libgc1_1%3a8.0.6-1.1build1_amd64.deb ...\n",
      "\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 24%]\u001b[49m\u001b[39m [##############............................................] \u001b8Unpacking libgc1:amd64 (1:8.0.6-1.1build1) ...\n",
      "\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 27%]\u001b[49m\u001b[39m [###############...........................................] \u001b8Selecting previously unselected package libobjc4:amd64.\n",
      "Preparing to unpack .../06-libobjc4_12.3.0-1ubuntu1~22.04_amd64.deb ...\n",
      "\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 29%]\u001b[49m\u001b[39m [################..........................................] \u001b8Unpacking libobjc4:amd64 (12.3.0-1ubuntu1~22.04) ...\n",
      "\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 31%]\u001b[49m\u001b[39m [##################........................................] \u001b8Selecting previously unselected package libgnustep-base1.28.\n",
      "Preparing to unpack .../07-libgnustep-base1.28_1.28.0-4build1_amd64.deb ...\n",
      "\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 33%]\u001b[49m\u001b[39m [###################.......................................] \u001b8Unpacking libgnustep-base1.28 (1.28.0-4build1) ...\n",
      "\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 36%]\u001b[49m\u001b[39m [####################......................................] \u001b8Selecting previously unselected package gnustep-base-runtime.\n",
      "Preparing to unpack .../08-gnustep-base-runtime_1.28.0-4build1_amd64.deb ...\n",
      "\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 38%]\u001b[49m\u001b[39m [#####################.....................................] \u001b8Unpacking gnustep-base-runtime (1.28.0-4build1) ...\n",
      "\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 40%]\u001b[49m\u001b[39m [#######################...................................] \u001b8Selecting previously unselected package libwavpack1:amd64.\n",
      "Preparing to unpack .../09-libwavpack1_5.4.0-1build2_amd64.deb ...\n",
      "\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 42%]\u001b[49m\u001b[39m [########################..................................] \u001b8Unpacking libwavpack1:amd64 (5.4.0-1build2) ...\n",
      "\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 44%]\u001b[49m\u001b[39m [#########################.................................] \u001b8Selecting previously unselected package unar.\n",
      "Preparing to unpack .../10-unar_1.10.1-2build11_amd64.deb ...\n",
      "\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 47%]\u001b[49m\u001b[39m [###########################...............................] \u001b8Unpacking unar (1.10.1-2build11) ...\n",
      "\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 49%]\u001b[49m\u001b[39m [############################..............................] \u001b8Setting up gnustep-common (2.9.0-3) ...\n",
      "\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 51%]\u001b[49m\u001b[39m [#############################.............................] \u001b8\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 53%]\u001b[49m\u001b[39m [##############################............................] \u001b8Setting up libavahi-common-data:amd64 (0.8-5ubuntu5.2) ...\n",
      "\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 56%]\u001b[49m\u001b[39m [################################..........................] \u001b8\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 58%]\u001b[49m\u001b[39m [#################################.........................] \u001b8Setting up gnustep-base-common (1.28.0-4build1) ...\n",
      "\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 60%]\u001b[49m\u001b[39m [##################################........................] \u001b8\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 62%]\u001b[49m\u001b[39m [####################################......................] \u001b8Setting up libgc1:amd64 (1:8.0.6-1.1build1) ...\n",
      "\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 64%]\u001b[49m\u001b[39m [#####################################.....................] \u001b8\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 67%]\u001b[49m\u001b[39m [######################################....................] \u001b8Setting up libwavpack1:amd64 (5.4.0-1build2) ...\n",
      "\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 69%]\u001b[49m\u001b[39m [#######################################...................] \u001b8\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 71%]\u001b[49m\u001b[39m [#########################################.................] \u001b8Setting up libavahi-common3:amd64 (0.8-5ubuntu5.2) ...\n",
      "\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 73%]\u001b[49m\u001b[39m [##########################################................] \u001b8\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 76%]\u001b[49m\u001b[39m [###########################################...............] \u001b8Setting up libobjc4:amd64 (12.3.0-1ubuntu1~22.04) ...\n",
      "\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 78%]\u001b[49m\u001b[39m [#############################################.............] \u001b8\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 80%]\u001b[49m\u001b[39m [##############################################............] \u001b8Setting up libavahi-client3:amd64 (0.8-5ubuntu5.2) ...\n",
      "\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 82%]\u001b[49m\u001b[39m [###############################################...........] \u001b8\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 84%]\u001b[49m\u001b[39m [################################################..........] \u001b8Setting up libgnustep-base1.28 (1.28.0-4build1) ...\n",
      "\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 87%]\u001b[49m\u001b[39m [##################################################........] \u001b8\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 89%]\u001b[49m\u001b[39m [###################################################.......] \u001b8Setting up gnustep-base-runtime (1.28.0-4build1) ...\n",
      "\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 91%]\u001b[49m\u001b[39m [####################################################......] \u001b8\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 93%]\u001b[49m\u001b[39m [######################################################....] \u001b8Setting up unar (1.10.1-2build11) ...\n",
      "\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 96%]\u001b[49m\u001b[39m [#######################################################...] \u001b8\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 98%]\u001b[49m\u001b[39m [########################################################..] \u001b8Processing triggers for man-db (2.10.2-1) ...\n",
      "Processing triggers for libc-bin (2.35-0ubuntu3.8) ...\n",
      "\n",
      "Scanning processes...                                                                            ]\n",
      "Scanning linux images...                                                        \n",
      "\n",
      "Running kernel seems to be up-to-date.\n",
      "\n",
      "No services need to be restarted.\n",
      "\n",
      "No containers need to be restarted.\n",
      "\n",
      "No user sessions are running outdated binaries.\n",
      "\n",
      "No VM guests are running outdated hypervisor (qemu) binaries on this host.\n",
      "--2024-08-20 16:18:42--  https://comp-public-prod.obs.cn-east-3.myhuaweicloud.com/dataset/2024/%E8%AE%AD%E7%BB%83%E9%9B%86%28%E6%9C%89%E6%A0%87%E6%B3%A8%E7%AC%AC%E4%B8%80%E6%89%B9%29.zip?AccessKeyId=583AINLNMLDRFK7CC1YM&Expires=1739168844&Signature=9iONBSJORCS8UNr2m/VZnc7yYno%3D\n",
      "Resolving comp-public-prod.obs.cn-east-3.myhuaweicloud.com (comp-public-prod.obs.cn-east-3.myhuaweicloud.com)... 121.36.239.135, 121.36.239.130, 121.36.239.140\n",
      "Connecting to comp-public-prod.obs.cn-east-3.myhuaweicloud.com (comp-public-prod.obs.cn-east-3.myhuaweicloud.com)|121.36.239.135|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 1948217460 (1.8G) [application/zip]\n",
      "Saving to: ‘训练集(有标注第一批).zip’\n",
      "\n",
      "训练集(有标注第一批 100%[===================>]   1.81G  71.5MB/s    in 26s     \n",
      "\n",
      "2024-08-20 16:19:08 (72.1 MB/s) - ‘训练集(有标注第一批).zip’ saved [1948217460/1948217460]\n",
      "\n",
      "--2024-08-20 16:19:15--  https://comp-public-prod.obs.cn-east-3.myhuaweicloud.com/dataset/2024/%E6%B5%8B%E8%AF%95%E9%9B%86.zip?AccessKeyId=583AINLNMLDRFK7CC1YM&Expires=1739168909&Signature=CRsB54VqOtrzIdUHC3ay0l2ZGNw%3D\n",
      "Resolving comp-public-prod.obs.cn-east-3.myhuaweicloud.com (comp-public-prod.obs.cn-east-3.myhuaweicloud.com)... 121.36.239.135, 121.36.239.140, 121.36.239.130\n",
      "Connecting to comp-public-prod.obs.cn-east-3.myhuaweicloud.com (comp-public-prod.obs.cn-east-3.myhuaweicloud.com)|121.36.239.135|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 1096422832 (1.0G) [application/zip]\n",
      "Saving to: ‘测试集.zip’\n",
      "\n",
      "测试集.zip          100%[===================>]   1.02G  69.0MB/s    in 16s     \n",
      "\n",
      "2024-08-20 16:19:32 (65.4 MB/s) - ‘测试集.zip’ saved [1096422832/1096422832]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!apt install zip unzip -y\n",
    "!apt install unar -y\n",
    "\n",
    "!wget \"https://comp-public-prod.obs.cn-east-3.myhuaweicloud.com/dataset/2024/%E8%AE%AD%E7%BB%83%E9%9B%86%28%E6%9C%89%E6%A0%87%E6%B3%A8%E7%AC%AC%E4%B8%80%E6%89%B9%29.zip?AccessKeyId=583AINLNMLDRFK7CC1YM&Expires=1739168844&Signature=9iONBSJORCS8UNr2m/VZnc7yYno%3D\" -O 训练集\\(有标注第一批\\).zip\n",
    "!unar -q 训练集\\(有标注第一批\\).zip\n",
    "\n",
    "!wget \"https://comp-public-prod.obs.cn-east-3.myhuaweicloud.com/dataset/2024/%E6%B5%8B%E8%AF%95%E9%9B%86.zip?AccessKeyId=583AINLNMLDRFK7CC1YM&Expires=1739168909&Signature=CRsB54VqOtrzIdUHC3ay0l2ZGNw%3D\" -O 测试集.zip\n",
    "!unar -q 测试集.zip"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b70fb82-5584-443e-b12a-5b45c1d1d0bf",
   "metadata": {},
   "source": [
    "## 数据读取"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9f8ecb98-b933-47c4-a947-e518402c2f46",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "({'frame_id': 0,\n",
       "  'event_id': 1,\n",
       "  'category': '非机动车违停',\n",
       "  'bbox': [746, 494, 988, 786]},\n",
       " 1688)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_anno = json.load(open('训练集(有标注第一批)/标注/45.json', encoding='utf-8'))\n",
    "train_anno[0], len(train_anno)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8b50a639-b875-47ea-97d0-622a05141d7e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>frame_id</th>\n",
       "      <th>event_id</th>\n",
       "      <th>category</th>\n",
       "      <th>bbox</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>非机动车违停</td>\n",
       "      <td>[746, 494, 988, 786]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>非机动车违停</td>\n",
       "      <td>[755, 606, 967, 843]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>非机动车违停</td>\n",
       "      <td>[502, 33, 829, 365]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>机动车违停</td>\n",
       "      <td>[549, 784, 1345, 1079]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>非机动车违停</td>\n",
       "      <td>[746, 494, 988, 786]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1683</th>\n",
       "      <td>420</td>\n",
       "      <td>4</td>\n",
       "      <td>机动车违停</td>\n",
       "      <td>[549, 784, 1345, 1079]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1684</th>\n",
       "      <td>421</td>\n",
       "      <td>4</td>\n",
       "      <td>机动车违停</td>\n",
       "      <td>[549, 784, 1345, 1079]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1685</th>\n",
       "      <td>421</td>\n",
       "      <td>1</td>\n",
       "      <td>非机动车违停</td>\n",
       "      <td>[746, 494, 988, 786]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1686</th>\n",
       "      <td>421</td>\n",
       "      <td>2</td>\n",
       "      <td>非机动车违停</td>\n",
       "      <td>[755, 606, 967, 843]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1687</th>\n",
       "      <td>421</td>\n",
       "      <td>3</td>\n",
       "      <td>非机动车违停</td>\n",
       "      <td>[502, 33, 829, 365]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1688 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      frame_id  event_id category                    bbox\n",
       "0            0         1   非机动车违停    [746, 494, 988, 786]\n",
       "1            0         2   非机动车违停    [755, 606, 967, 843]\n",
       "2            0         3   非机动车违停     [502, 33, 829, 365]\n",
       "3            0         4    机动车违停  [549, 784, 1345, 1079]\n",
       "4            1         1   非机动车违停    [746, 494, 988, 786]\n",
       "...        ...       ...      ...                     ...\n",
       "1683       420         4    机动车违停  [549, 784, 1345, 1079]\n",
       "1684       421         4    机动车违停  [549, 784, 1345, 1079]\n",
       "1685       421         1   非机动车违停    [746, 494, 988, 786]\n",
       "1686       421         2   非机动车违停    [755, 606, 967, 843]\n",
       "1687       421         3   非机动车违停     [502, 33, 829, 365]\n",
       "\n",
       "[1688 rows x 4 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_json('训练集(有标注第一批)/标注/45.json')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "65ccf434-bf1d-4dc9-bbfc-91d39aa108fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "video_path = '训练集(有标注第一批)/视频/45.mp4'\n",
    "cap = cv2.VideoCapture(video_path)\n",
    "while True:\n",
    "    # 读取下一帧\n",
    "    ret, frame = cap.read()\n",
    "    if not ret:\n",
    "        break\n",
    "    break    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2fae612a-9c4d-422e-848e-1001750fc6a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1080, 1920, 3)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d564f6cf-de70-41fb-9602-69d6d075b640",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "422"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "int(cap.get(cv2.CAP_PROP_FRAME_COUNT))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5593961a-9d32-47e4-904b-f7b0d1cd3b3d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f4f5f27afe0>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bbox = [746, 494, 988, 786]\n",
    "\n",
    "pt1 = (bbox[0], bbox[1])\n",
    "pt2 = (bbox[2], bbox[3])\n",
    "\n",
    "color = (0, 255, 0) \n",
    "thickness = 2  # 线条粗细\n",
    "\n",
    "cv2.rectangle(frame, pt1, pt2, color, thickness)\n",
    "\n",
    "frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)\n",
    "plt.imshow(frame)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a5e8e3d-6ae3-4ab6-9869-1cb89b51f861",
   "metadata": {},
   "source": [
    "## 数据转换"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1546204b-aa4e-44cf-95a7-dc6f4f8eab49",
   "metadata": {},
   "outputs": [],
   "source": [
    "if not os.path.exists('yolo-dataset/'):\n",
    "    os.mkdir('yolo-dataset/')\n",
    "if not os.path.exists('yolo-dataset/train'):\n",
    "    os.mkdir('yolo-dataset/train')\n",
    "if not os.path.exists('yolo-dataset/val'):\n",
    "    os.mkdir('yolo-dataset/val')\n",
    "\n",
    "dir_path = os.path.abspath('./') + '/'\n",
    "\n",
    "# 需要按照你的修改path\n",
    "with open('yolo-dataset/yolo.yaml', 'w', encoding='utf-8') as up:\n",
    "    up.write(f'''\n",
    "path: {dir_path}/yolo-dataset/\n",
    "train: train/\n",
    "val: val/\n",
    "\n",
    "names:\n",
    "    0: 非机动车违停\n",
    "    1: 机动车违停\n",
    "    2: 垃圾桶满溢\n",
    "    3: 违法经营\n",
    "''')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ae2ca2fc-5503-41f8-8dfc-9e0d385c56da",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_annos = glob.glob('训练集(有标注第一批)/标注/*.json')\n",
    "train_videos = glob.glob('训练集(有标注第一批)/视频/*.mp4')\n",
    "train_annos.sort(); train_videos.sort();\n",
    "\n",
    "category_labels = [\"非机动车违停\", \"机动车违停\", \"垃圾桶满溢\", \"违法经营\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "98f90f73-cf97-4121-8c1e-d3f85e065493",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "训练集(有标注第一批)/视频/0.mp4\n",
      "训练集(有标注第一批)/视频/1.mp4\n",
      "训练集(有标注第一批)/视频/10.mp4\n",
      "训练集(有标注第一批)/视频/11.mp4\n",
      "训练集(有标注第一批)/视频/12.mp4\n"
     ]
    }
   ],
   "source": [
    "for anno_path, video_path in zip(train_annos[:5], train_videos[:5]):\n",
    "    print(video_path)\n",
    "    anno_df = pd.read_json(anno_path)\n",
    "    cap = cv2.VideoCapture(video_path)\n",
    "    frame_idx = 0 \n",
    "    while True:\n",
    "        ret, frame = cap.read()\n",
    "        if not ret:\n",
    "            break\n",
    "\n",
    "        img_height, img_width = frame.shape[:2]\n",
    "        \n",
    "        frame_anno = anno_df[anno_df['frame_id'] == frame_idx]\n",
    "        cv2.imwrite('./yolo-dataset/train/' + anno_path.split('/')[-1][:-5] + '_' + str(frame_idx) + '.jpg', frame)\n",
    "\n",
    "        if len(frame_anno) != 0:\n",
    "            with open('./yolo-dataset/train/' + anno_path.split('/')[-1][:-5] + '_' + str(frame_idx) + '.txt', 'w') as up:\n",
    "                for category, bbox in zip(frame_anno['category'].values, frame_anno['bbox'].values):\n",
    "                    category_idx = category_labels.index(category)\n",
    "                    \n",
    "                    x_min, y_min, x_max, y_max = bbox\n",
    "                    x_center = (x_min + x_max) / 2 / img_width\n",
    "                    y_center = (y_min + y_max) / 2 / img_height\n",
    "                    width = (x_max - x_min) / img_width\n",
    "                    height = (y_max - y_min) / img_height\n",
    "\n",
    "                    if x_center > 1:\n",
    "                        print(bbox)\n",
    "                    up.write(f'{category_idx} {x_center} {y_center} {width} {height}\\n')\n",
    "        \n",
    "        frame_idx += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "5ac6aedc-844c-4c85-9c0d-e7c9b65724d6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "训练集(有标注第一批)/视频/7.mp4\n",
      "训练集(有标注第一批)/视频/8.mp4\n",
      "训练集(有标注第一批)/视频/9.mp4\n"
     ]
    }
   ],
   "source": [
    "for anno_path, video_path in zip(train_annos[-3:], train_videos[-3:]):\n",
    "    print(video_path)\n",
    "    anno_df = pd.read_json(anno_path)\n",
    "    cap = cv2.VideoCapture(video_path)\n",
    "    frame_idx = 0 \n",
    "    while True:\n",
    "        ret, frame = cap.read()\n",
    "        if not ret:\n",
    "            break\n",
    "\n",
    "        img_height, img_width = frame.shape[:2]\n",
    "        \n",
    "        frame_anno = anno_df[anno_df['frame_id'] == frame_idx]\n",
    "        cv2.imwrite('./yolo-dataset/val/' + anno_path.split('/')[-1][:-5] + '_' + str(frame_idx) + '.jpg', frame)\n",
    "\n",
    "        if len(frame_anno) != 0:\n",
    "            with open('./yolo-dataset/val/' + anno_path.split('/')[-1][:-5] + '_' + str(frame_idx) + '.txt', 'w') as up:\n",
    "                for category, bbox in zip(frame_anno['category'].values, frame_anno['bbox'].values):\n",
    "                    category_idx = category_labels.index(category)\n",
    "                    \n",
    "                    x_min, y_min, x_max, y_max = bbox\n",
    "                    x_center = (x_min + x_max) / 2 / img_width\n",
    "                    y_center = (y_min + y_max) / 2 / img_height\n",
    "                    width = (x_max - x_min) / img_width\n",
    "                    height = (y_max - y_min) / img_height\n",
    "\n",
    "                    up.write(f'{category_idx} {x_center} {y_center} {width} {height}\\n')\n",
    "        \n",
    "        frame_idx += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "4001626c-e990-4fe3-bb71-3d7e475c4188",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2024-08-20 16:19:51--  http://mirror.coggle.club/yolo/yolov8n-v8.2.0.pt\n",
      "Resolving mirror.coggle.club (mirror.coggle.club)... 101.206.244.158, 101.206.244.154, 101.206.244.155, ...\n",
      "Connecting to mirror.coggle.club (mirror.coggle.club)|101.206.244.158|:80... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 6549796 (6.2M) [application/vnd.snesdev-page-table]\n",
      "Saving to: ‘yolov8n.pt’\n",
      "\n",
      "yolov8n.pt          100%[===================>]   6.25M  11.4MB/s    in 0.5s    \n",
      "\n",
      "2024-08-20 16:19:53 (11.4 MB/s) - ‘yolov8n.pt’ saved [6549796/6549796]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!wget http://mirror.coggle.club/yolo/yolov8n-v8.2.0.pt -O yolov8n.pt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e020c6bb-8fd3-4dbb-824c-cb92e708ee48",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2024-08-20 16:19:53--  http://mirror.coggle.club/yolo/Arial.ttf\n",
      "Resolving mirror.coggle.club (mirror.coggle.club)... 101.206.244.156, 101.206.244.158, 101.206.244.157, ...\n",
      "Connecting to mirror.coggle.club (mirror.coggle.club)|101.206.244.156|:80... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 773236 (755K) [font/ttf]\n",
      "Saving to: ‘/root/.config/Ultralytics/Arial.ttf’\n",
      "\n",
      "/root/.config/Ultra 100%[===================>] 755.11K  4.88MB/s    in 0.2s    \n",
      "\n",
      "2024-08-20 16:19:53 (4.88 MB/s) - ‘/root/.config/Ultralytics/Arial.ttf’ saved [773236/773236]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!mkdir -p ~/.config/Ultralytics/\n",
    "!wget http://mirror.coggle.club/yolo/Arial.ttf -O ~/.config/Ultralytics/Arial.ttf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "568eb32b-8ab6-4211-b74c-09b2a9a6c65c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ultralytics YOLOv8.2.79 🚀 Python-3.10.13 torch-2.2.2+cu118 CUDA:0 (NVIDIA GeForce RTX 4090, 24217MiB)\n",
      "\u001b[34m\u001b[1mengine/trainer: \u001b[0mtask=detect, mode=train, model=yolov8n.pt, data=yolo-dataset/yolo.yaml, epochs=2, time=None, patience=100, batch=16, imgsz=1080, save=True, save_period=-1, cache=False, device=None, workers=8, project=None, name=train, exist_ok=False, pretrained=True, optimizer=auto, verbose=True, seed=0, deterministic=True, single_cls=False, rect=False, cos_lr=False, close_mosaic=10, resume=False, amp=True, fraction=1.0, profile=False, freeze=None, multi_scale=False, overlap_mask=True, mask_ratio=4, dropout=0.0, val=True, split=val, save_json=False, save_hybrid=False, conf=None, iou=0.7, max_det=300, half=False, dnn=False, plots=True, source=None, vid_stride=1, stream_buffer=False, visualize=False, augment=False, agnostic_nms=False, classes=None, retina_masks=False, embed=None, show=False, save_frames=False, save_txt=False, save_conf=False, save_crop=False, show_labels=True, show_conf=True, show_boxes=True, line_width=None, format=torchscript, keras=False, optimize=False, int8=False, dynamic=False, simplify=False, opset=None, workspace=4, nms=False, lr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=7.5, cls=0.5, dfl=1.5, pose=12.0, kobj=1.0, label_smoothing=0.0, nbs=64, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, bgr=0.0, mosaic=1.0, mixup=0.0, copy_paste=0.0, auto_augment=randaugment, erasing=0.4, crop_fraction=1.0, cfg=None, tracker=botsort.yaml, save_dir=runs/detect/train\n",
      "Downloading https://ultralytics.com/assets/Arial.Unicode.ttf to '/root/.config/Ultralytics/Arial.Unicode.ttf'...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 22.2M/22.2M [00:00<00:00, 26.8MB/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overriding model.yaml nc=80 with nc=4\n",
      "\n",
      "                   from  n    params  module                                       arguments                     \n",
      "  0                  -1  1       464  ultralytics.nn.modules.conv.Conv             [3, 16, 3, 2]                 \n",
      "  1                  -1  1      4672  ultralytics.nn.modules.conv.Conv             [16, 32, 3, 2]                \n",
      "  2                  -1  1      7360  ultralytics.nn.modules.block.C2f             [32, 32, 1, True]             \n",
      "  3                  -1  1     18560  ultralytics.nn.modules.conv.Conv             [32, 64, 3, 2]                \n",
      "  4                  -1  2     49664  ultralytics.nn.modules.block.C2f             [64, 64, 2, True]             \n",
      "  5                  -1  1     73984  ultralytics.nn.modules.conv.Conv             [64, 128, 3, 2]               \n",
      "  6                  -1  2    197632  ultralytics.nn.modules.block.C2f             [128, 128, 2, True]           \n",
      "  7                  -1  1    295424  ultralytics.nn.modules.conv.Conv             [128, 256, 3, 2]              \n",
      "  8                  -1  1    460288  ultralytics.nn.modules.block.C2f             [256, 256, 1, True]           \n",
      "  9                  -1  1    164608  ultralytics.nn.modules.block.SPPF            [256, 256, 5]                 \n",
      " 10                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']          \n",
      " 11             [-1, 6]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           \n",
      " 12                  -1  1    148224  ultralytics.nn.modules.block.C2f             [384, 128, 1]                 \n",
      " 13                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']          \n",
      " 14             [-1, 4]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           \n",
      " 15                  -1  1     37248  ultralytics.nn.modules.block.C2f             [192, 64, 1]                  \n",
      " 16                  -1  1     36992  ultralytics.nn.modules.conv.Conv             [64, 64, 3, 2]                \n",
      " 17            [-1, 12]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           \n",
      " 18                  -1  1    123648  ultralytics.nn.modules.block.C2f             [192, 128, 1]                 \n",
      " 19                  -1  1    147712  ultralytics.nn.modules.conv.Conv             [128, 128, 3, 2]              \n",
      " 20             [-1, 9]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           \n",
      " 21                  -1  1    493056  ultralytics.nn.modules.block.C2f             [384, 256, 1]                 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 22        [15, 18, 21]  1    752092  ultralytics.nn.modules.head.Detect           [4, [64, 128, 256]]           \n",
      "Model summary: 225 layers, 3,011,628 parameters, 3,011,612 gradients, 8.2 GFLOPs\n",
      "\n",
      "Transferred 319/355 items from pretrained weights\n",
      "Freezing layer 'model.22.dfl.conv.weight'\n",
      "\u001b[34m\u001b[1mAMP: \u001b[0mrunning Automatic Mixed Precision (AMP) checks with YOLOv8n...\n",
      "\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n",
      "WARNING ⚠️ imgsz=[1080] must be multiple of max stride 32, updating to [1088]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mtrain: \u001b[0mScanning /root/yolo-dataset/train... 676 images, 0 backgrounds, 0 corrupt: 100%|██████████| 676/676 [00:00<00:00, 1965.12it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mtrain: \u001b[0mNew cache created: /root/yolo-dataset/train.cache\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "\u001b[34m\u001b[1mval: \u001b[0mScanning /root/yolo-dataset/val... 580 images, 3 backgrounds, 0 corrupt: 100%|██████████| 583/583 [00:00<00:00, 2313.73it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mval: \u001b[0mNew cache created: /root/yolo-dataset/val.cache\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plotting labels to runs/detect/train/labels.jpg... \n",
      "\u001b[34m\u001b[1moptimizer:\u001b[0m 'optimizer=auto' found, ignoring 'lr0=0.01' and 'momentum=0.937' and determining best 'optimizer', 'lr0' and 'momentum' automatically... \n",
      "\u001b[34m\u001b[1moptimizer:\u001b[0m AdamW(lr=0.00125, momentum=0.9) with parameter groups 57 weight(decay=0.0), 64 weight(decay=0.0005), 63 bias(decay=0.0)\n",
      "Image sizes 1088 train, 1088 val\n",
      "Using 8 dataloader workers\n",
      "Logging results to \u001b[1mruns/detect/train\u001b[0m\n",
      "Starting training for 2 epochs...\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "        1/2      6.62G      1.529       3.15      1.241         20       1088: 100%|██████████| 43/43 [00:07<00:00,  6.13it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 19/19 [00:03<00:00,  5.76it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all        583       6590     0.0225      0.355     0.0576     0.0224\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "        2/2      6.25G     0.8006      1.357     0.9097         29       1088: 100%|██████████| 43/43 [00:04<00:00, 10.74it/s]\n",
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 19/19 [00:02<00:00,  9.49it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all        583       6590     0.0398      0.547      0.175     0.0802\n",
      "\n",
      "2 epochs completed in 0.005 hours.\n",
      "Optimizer stripped from runs/detect/train/weights/last.pt, 6.3MB\n",
      "Optimizer stripped from runs/detect/train/weights/best.pt, 6.3MB\n",
      "\n",
      "Validating runs/detect/train/weights/best.pt...\n",
      "Ultralytics YOLOv8.2.79 🚀 Python-3.10.13 torch-2.2.2+cu118 CUDA:0 (NVIDIA GeForce RTX 4090, 24217MiB)\n",
      "Model summary (fused): 168 layers, 3,006,428 parameters, 0 gradients, 8.1 GFLOPs\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 19/19 [00:03<00:00,  6.18it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   all        583       6590     0.0398      0.546      0.175     0.0801\n",
      "                非机动车违停        580       2031     0.0215      0.691      0.272      0.102\n",
      "                 机动车违停        580       3515      0.128      0.678      0.385      0.211\n",
      "                 垃圾桶满溢        580        580     0.0052      0.334    0.00349    0.00269\n",
      "                  违法经营        232        464    0.00416      0.483     0.0407    0.00427\n",
      "Speed: 0.1ms preprocess, 0.5ms inference, 0.0ms loss, 1.7ms postprocess per image\n",
      "Results saved to \u001b[1mruns/detect/train\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"0\"\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "\n",
    "from ultralytics import YOLO\n",
    "model = YOLO(\"yolov8n.pt\")\n",
    "results = model.train(data=\"yolo-dataset/yolo.yaml\", epochs=2, imgsz=1080, batch=16)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "1b13f176-408c-4c27-a558-8506078cd1c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "category_labels = [\"非机动车违停\", \"机动车违停\", \"垃圾桶满溢\", \"违法经营\"]\n",
    "\n",
    "if not os.path.exists('result/'):\n",
    "    os.mkdir('result')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "524c2dae-5402-4644-a6d7-a92d58dac5c6",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING ⚠️ imgsz=[1080] must be multiple of max stride 32, updating to [1088]\n",
      "\n",
      "WARNING ⚠️ inference results will accumulate in RAM unless `stream=True` is passed, causing potential out-of-memory\n",
      "errors for large sources or long-running streams and videos. See https://docs.ultralytics.com/modes/predict/ for help.\n",
      "\n",
      "Example:\n",
      "    results = model(source=..., stream=True)  # generator of Results objects\n",
      "    for r in results:\n",
      "        boxes = r.boxes  # Boxes object for bbox outputs\n",
      "        masks = r.masks  # Masks object for segment masks outputs\n",
      "        probs = r.probs  # Class probabilities for classification outputs\n",
      "\n",
      "WARNING ⚠️ imgsz=[1080] must be multiple of max stride 32, updating to [1088]\n",
      "\n",
      "WARNING ⚠️ inference results will accumulate in RAM unless `stream=True` is passed, causing potential out-of-memory\n",
      "errors for large sources or long-running streams and videos. See https://docs.ultralytics.com/modes/predict/ for help.\n",
      "\n",
      "Example:\n",
      "    results = model(source=..., stream=True)  # generator of Results objects\n",
      "    for r in results:\n",
      "        boxes = r.boxes  # Boxes object for bbox outputs\n",
      "        masks = r.masks  # Masks object for segment masks outputs\n",
      "        probs = r.probs  # Class probabilities for classification outputs\n",
      "\n",
      "WARNING ⚠️ imgsz=[1080] must be multiple of max stride 32, updating to [1088]\n",
      "\n",
      "WARNING ⚠️ inference results will accumulate in RAM unless `stream=True` is passed, causing potential out-of-memory\n",
      "errors for large sources or long-running streams and videos. See https://docs.ultralytics.com/modes/predict/ for help.\n",
      "\n",
      "Example:\n",
      "    results = model(source=..., stream=True)  # generator of Results objects\n",
      "    for r in results:\n",
      "        boxes = r.boxes  # Boxes object for bbox outputs\n",
      "        masks = r.masks  # Masks object for segment masks outputs\n",
      "        probs = r.probs  # Class probabilities for classification outputs\n",
      "\n",
      "WARNING ⚠️ imgsz=[1080] must be multiple of max stride 32, updating to [1088]\n",
      "\n",
      "WARNING ⚠️ inference results will accumulate in RAM unless `stream=True` is passed, causing potential out-of-memory\n",
      "errors for large sources or long-running streams and videos. See https://docs.ultralytics.com/modes/predict/ for help.\n",
      "\n",
      "Example:\n",
      "    results = model(source=..., stream=True)  # generator of Results objects\n",
      "    for r in results:\n",
      "        boxes = r.boxes  # Boxes object for bbox outputs\n",
      "        masks = r.masks  # Masks object for segment masks outputs\n",
      "        probs = r.probs  # Class probabilities for classification outputs\n",
      "\n",
      "WARNING ⚠️ imgsz=[1080] must be multiple of max stride 32, updating to [1088]\n",
      "\n",
      "WARNING ⚠️ inference results will accumulate in RAM unless `stream=True` is passed, causing potential out-of-memory\n",
      "errors for large sources or long-running streams and videos. See https://docs.ultralytics.com/modes/predict/ for help.\n",
      "\n",
      "Example:\n",
      "    results = model(source=..., stream=True)  # generator of Results objects\n",
      "    for r in results:\n",
      "        boxes = r.boxes  # Boxes object for bbox outputs\n",
      "        masks = r.masks  # Masks object for segment masks outputs\n",
      "        probs = r.probs  # Class probabilities for classification outputs\n",
      "\n",
      "WARNING ⚠️ imgsz=[1080] must be multiple of max stride 32, updating to [1088]\n",
      "\n",
      "WARNING ⚠️ inference results will accumulate in RAM unless `stream=True` is passed, causing potential out-of-memory\n",
      "errors for large sources or long-running streams and videos. See https://docs.ultralytics.com/modes/predict/ for help.\n",
      "\n",
      "Example:\n",
      "    results = model(source=..., stream=True)  # generator of Results objects\n",
      "    for r in results:\n",
      "        boxes = r.boxes  # Boxes object for bbox outputs\n",
      "        masks = r.masks  # Masks object for segment masks outputs\n",
      "        probs = r.probs  # Class probabilities for classification outputs\n",
      "\n",
      "WARNING ⚠️ imgsz=[1080] must be multiple of max stride 32, updating to [1088]\n",
      "\n",
      "WARNING ⚠️ inference results will accumulate in RAM unless `stream=True` is passed, causing potential out-of-memory\n",
      "errors for large sources or long-running streams and videos. See https://docs.ultralytics.com/modes/predict/ for help.\n",
      "\n",
      "Example:\n",
      "    results = model(source=..., stream=True)  # generator of Results objects\n",
      "    for r in results:\n",
      "        boxes = r.boxes  # Boxes object for bbox outputs\n",
      "        masks = r.masks  # Masks object for segment masks outputs\n",
      "        probs = r.probs  # Class probabilities for classification outputs\n",
      "\n",
      "WARNING ⚠️ imgsz=[1080] must be multiple of max stride 32, updating to [1088]\n",
      "\n",
      "WARNING ⚠️ inference results will accumulate in RAM unless `stream=True` is passed, causing potential out-of-memory\n",
      "errors for large sources or long-running streams and videos. See https://docs.ultralytics.com/modes/predict/ for help.\n",
      "\n",
      "Example:\n",
      "    results = model(source=..., stream=True)  # generator of Results objects\n",
      "    for r in results:\n",
      "        boxes = r.boxes  # Boxes object for bbox outputs\n",
      "        masks = r.masks  # Masks object for segment masks outputs\n",
      "        probs = r.probs  # Class probabilities for classification outputs\n",
      "\n",
      "WARNING ⚠️ imgsz=[1080] must be multiple of max stride 32, updating to [1088]\n",
      "\n",
      "WARNING ⚠️ inference results will accumulate in RAM unless `stream=True` is passed, causing potential out-of-memory\n",
      "errors for large sources or long-running streams and videos. See https://docs.ultralytics.com/modes/predict/ for help.\n",
      "\n",
      "Example:\n",
      "    results = model(source=..., stream=True)  # generator of Results objects\n",
      "    for r in results:\n",
      "        boxes = r.boxes  # Boxes object for bbox outputs\n",
      "        masks = r.masks  # Masks object for segment masks outputs\n",
      "        probs = r.probs  # Class probabilities for classification outputs\n",
      "\n",
      "WARNING ⚠️ imgsz=[1080] must be multiple of max stride 32, updating to [1088]\n",
      "\n",
      "WARNING ⚠️ inference results will accumulate in RAM unless `stream=True` is passed, causing potential out-of-memory\n",
      "errors for large sources or long-running streams and videos. See https://docs.ultralytics.com/modes/predict/ for help.\n",
      "\n",
      "Example:\n",
      "    results = model(source=..., stream=True)  # generator of Results objects\n",
      "    for r in results:\n",
      "        boxes = r.boxes  # Boxes object for bbox outputs\n",
      "        masks = r.masks  # Masks object for segment masks outputs\n",
      "        probs = r.probs  # Class probabilities for classification outputs\n",
      "\n",
      "WARNING ⚠️ imgsz=[1080] must be multiple of max stride 32, updating to [1088]\n",
      "\n",
      "WARNING ⚠️ inference results will accumulate in RAM unless `stream=True` is passed, causing potential out-of-memory\n",
      "errors for large sources or long-running streams and videos. See https://docs.ultralytics.com/modes/predict/ for help.\n",
      "\n",
      "Example:\n",
      "    results = model(source=..., stream=True)  # generator of Results objects\n",
      "    for r in results:\n",
      "        boxes = r.boxes  # Boxes object for bbox outputs\n",
      "        masks = r.masks  # Masks object for segment masks outputs\n",
      "        probs = r.probs  # Class probabilities for classification outputs\n",
      "\n",
      "WARNING ⚠️ imgsz=[1080] must be multiple of max stride 32, updating to [1088]\n",
      "\n",
      "WARNING ⚠️ inference results will accumulate in RAM unless `stream=True` is passed, causing potential out-of-memory\n",
      "errors for large sources or long-running streams and videos. See https://docs.ultralytics.com/modes/predict/ for help.\n",
      "\n",
      "Example:\n",
      "    results = model(source=..., stream=True)  # generator of Results objects\n",
      "    for r in results:\n",
      "        boxes = r.boxes  # Boxes object for bbox outputs\n",
      "        masks = r.masks  # Masks object for segment masks outputs\n",
      "        probs = r.probs  # Class probabilities for classification outputs\n",
      "\n",
      "WARNING ⚠️ imgsz=[1080] must be multiple of max stride 32, updating to [1088]\n",
      "\n",
      "WARNING ⚠️ inference results will accumulate in RAM unless `stream=True` is passed, causing potential out-of-memory\n",
      "errors for large sources or long-running streams and videos. See https://docs.ultralytics.com/modes/predict/ for help.\n",
      "\n",
      "Example:\n",
      "    results = model(source=..., stream=True)  # generator of Results objects\n",
      "    for r in results:\n",
      "        boxes = r.boxes  # Boxes object for bbox outputs\n",
      "        masks = r.masks  # Masks object for segment masks outputs\n",
      "        probs = r.probs  # Class probabilities for classification outputs\n",
      "\n",
      "WARNING ⚠️ imgsz=[1080] must be multiple of max stride 32, updating to [1088]\n",
      "\n",
      "WARNING ⚠️ inference results will accumulate in RAM unless `stream=True` is passed, causing potential out-of-memory\n",
      "errors for large sources or long-running streams and videos. See https://docs.ultralytics.com/modes/predict/ for help.\n",
      "\n",
      "Example:\n",
      "    results = model(source=..., stream=True)  # generator of Results objects\n",
      "    for r in results:\n",
      "        boxes = r.boxes  # Boxes object for bbox outputs\n",
      "        masks = r.masks  # Masks object for segment masks outputs\n",
      "        probs = r.probs  # Class probabilities for classification outputs\n",
      "\n",
      "WARNING ⚠️ imgsz=[1080] must be multiple of max stride 32, updating to [1088]\n",
      "\n",
      "WARNING ⚠️ inference results will accumulate in RAM unless `stream=True` is passed, causing potential out-of-memory\n",
      "errors for large sources or long-running streams and videos. See https://docs.ultralytics.com/modes/predict/ for help.\n",
      "\n",
      "Example:\n",
      "    results = model(source=..., stream=True)  # generator of Results objects\n",
      "    for r in results:\n",
      "        boxes = r.boxes  # Boxes object for bbox outputs\n",
      "        masks = r.masks  # Masks object for segment masks outputs\n",
      "        probs = r.probs  # Class probabilities for classification outputs\n",
      "\n",
      "WARNING ⚠️ imgsz=[1080] must be multiple of max stride 32, updating to [1088]\n",
      "\n",
      "WARNING ⚠️ inference results will accumulate in RAM unless `stream=True` is passed, causing potential out-of-memory\n",
      "errors for large sources or long-running streams and videos. See https://docs.ultralytics.com/modes/predict/ for help.\n",
      "\n",
      "Example:\n",
      "    results = model(source=..., stream=True)  # generator of Results objects\n",
      "    for r in results:\n",
      "        boxes = r.boxes  # Boxes object for bbox outputs\n",
      "        masks = r.masks  # Masks object for segment masks outputs\n",
      "        probs = r.probs  # Class probabilities for classification outputs\n",
      "\n",
      "WARNING ⚠️ imgsz=[1080] must be multiple of max stride 32, updating to [1088]\n",
      "\n",
      "WARNING ⚠️ inference results will accumulate in RAM unless `stream=True` is passed, causing potential out-of-memory\n",
      "errors for large sources or long-running streams and videos. See https://docs.ultralytics.com/modes/predict/ for help.\n",
      "\n",
      "Example:\n",
      "    results = model(source=..., stream=True)  # generator of Results objects\n",
      "    for r in results:\n",
      "        boxes = r.boxes  # Boxes object for bbox outputs\n",
      "        masks = r.masks  # Masks object for segment masks outputs\n",
      "        probs = r.probs  # Class probabilities for classification outputs\n",
      "\n",
      "WARNING ⚠️ imgsz=[1080] must be multiple of max stride 32, updating to [1088]\n",
      "\n",
      "WARNING ⚠️ inference results will accumulate in RAM unless `stream=True` is passed, causing potential out-of-memory\n",
      "errors for large sources or long-running streams and videos. See https://docs.ultralytics.com/modes/predict/ for help.\n",
      "\n",
      "Example:\n",
      "    results = model(source=..., stream=True)  # generator of Results objects\n",
      "    for r in results:\n",
      "        boxes = r.boxes  # Boxes object for bbox outputs\n",
      "        masks = r.masks  # Masks object for segment masks outputs\n",
      "        probs = r.probs  # Class probabilities for classification outputs\n",
      "\n",
      "WARNING ⚠️ imgsz=[1080] must be multiple of max stride 32, updating to [1088]\n",
      "\n",
      "WARNING ⚠️ inference results will accumulate in RAM unless `stream=True` is passed, causing potential out-of-memory\n",
      "errors for large sources or long-running streams and videos. See https://docs.ultralytics.com/modes/predict/ for help.\n",
      "\n",
      "Example:\n",
      "    results = model(source=..., stream=True)  # generator of Results objects\n",
      "    for r in results:\n",
      "        boxes = r.boxes  # Boxes object for bbox outputs\n",
      "        masks = r.masks  # Masks object for segment masks outputs\n",
      "        probs = r.probs  # Class probabilities for classification outputs\n",
      "\n",
      "WARNING ⚠️ imgsz=[1080] must be multiple of max stride 32, updating to [1088]\n",
      "\n",
      "WARNING ⚠️ inference results will accumulate in RAM unless `stream=True` is passed, causing potential out-of-memory\n",
      "errors for large sources or long-running streams and videos. See https://docs.ultralytics.com/modes/predict/ for help.\n",
      "\n",
      "Example:\n",
      "    results = model(source=..., stream=True)  # generator of Results objects\n",
      "    for r in results:\n",
      "        boxes = r.boxes  # Boxes object for bbox outputs\n",
      "        masks = r.masks  # Masks object for segment masks outputs\n",
      "        probs = r.probs  # Class probabilities for classification outputs\n",
      "\n",
      "WARNING ⚠️ imgsz=[1080] must be multiple of max stride 32, updating to [1088]\n",
      "\n",
      "WARNING ⚠️ inference results will accumulate in RAM unless `stream=True` is passed, causing potential out-of-memory\n",
      "errors for large sources or long-running streams and videos. See https://docs.ultralytics.com/modes/predict/ for help.\n",
      "\n",
      "Example:\n",
      "    results = model(source=..., stream=True)  # generator of Results objects\n",
      "    for r in results:\n",
      "        boxes = r.boxes  # Boxes object for bbox outputs\n",
      "        masks = r.masks  # Masks object for segment masks outputs\n",
      "        probs = r.probs  # Class probabilities for classification outputs\n",
      "\n",
      "WARNING ⚠️ imgsz=[1080] must be multiple of max stride 32, updating to [1088]\n",
      "\n",
      "WARNING ⚠️ inference results will accumulate in RAM unless `stream=True` is passed, causing potential out-of-memory\n",
      "errors for large sources or long-running streams and videos. See https://docs.ultralytics.com/modes/predict/ for help.\n",
      "\n",
      "Example:\n",
      "    results = model(source=..., stream=True)  # generator of Results objects\n",
      "    for r in results:\n",
      "        boxes = r.boxes  # Boxes object for bbox outputs\n",
      "        masks = r.masks  # Masks object for segment masks outputs\n",
      "        probs = r.probs  # Class probabilities for classification outputs\n",
      "\n",
      "WARNING ⚠️ imgsz=[1080] must be multiple of max stride 32, updating to [1088]\n",
      "\n",
      "WARNING ⚠️ inference results will accumulate in RAM unless `stream=True` is passed, causing potential out-of-memory\n",
      "errors for large sources or long-running streams and videos. See https://docs.ultralytics.com/modes/predict/ for help.\n",
      "\n",
      "Example:\n",
      "    results = model(source=..., stream=True)  # generator of Results objects\n",
      "    for r in results:\n",
      "        boxes = r.boxes  # Boxes object for bbox outputs\n",
      "        masks = r.masks  # Masks object for segment masks outputs\n",
      "        probs = r.probs  # Class probabilities for classification outputs\n",
      "\n",
      "WARNING ⚠️ imgsz=[1080] must be multiple of max stride 32, updating to [1088]\n",
      "\n",
      "WARNING ⚠️ inference results will accumulate in RAM unless `stream=True` is passed, causing potential out-of-memory\n",
      "errors for large sources or long-running streams and videos. See https://docs.ultralytics.com/modes/predict/ for help.\n",
      "\n",
      "Example:\n",
      "    results = model(source=..., stream=True)  # generator of Results objects\n",
      "    for r in results:\n",
      "        boxes = r.boxes  # Boxes object for bbox outputs\n",
      "        masks = r.masks  # Masks object for segment masks outputs\n",
      "        probs = r.probs  # Class probabilities for classification outputs\n",
      "\n",
      "WARNING ⚠️ imgsz=[1080] must be multiple of max stride 32, updating to [1088]\n",
      "\n",
      "WARNING ⚠️ inference results will accumulate in RAM unless `stream=True` is passed, causing potential out-of-memory\n",
      "errors for large sources or long-running streams and videos. See https://docs.ultralytics.com/modes/predict/ for help.\n",
      "\n",
      "Example:\n",
      "    results = model(source=..., stream=True)  # generator of Results objects\n",
      "    for r in results:\n",
      "        boxes = r.boxes  # Boxes object for bbox outputs\n",
      "        masks = r.masks  # Masks object for segment masks outputs\n",
      "        probs = r.probs  # Class probabilities for classification outputs\n",
      "\n",
      "WARNING ⚠️ imgsz=[1080] must be multiple of max stride 32, updating to [1088]\n",
      "\n",
      "WARNING ⚠️ inference results will accumulate in RAM unless `stream=True` is passed, causing potential out-of-memory\n",
      "errors for large sources or long-running streams and videos. See https://docs.ultralytics.com/modes/predict/ for help.\n",
      "\n",
      "Example:\n",
      "    results = model(source=..., stream=True)  # generator of Results objects\n",
      "    for r in results:\n",
      "        boxes = r.boxes  # Boxes object for bbox outputs\n",
      "        masks = r.masks  # Masks object for segment masks outputs\n",
      "        probs = r.probs  # Class probabilities for classification outputs\n",
      "\n",
      "WARNING ⚠️ imgsz=[1080] must be multiple of max stride 32, updating to [1088]\n",
      "\n",
      "WARNING ⚠️ inference results will accumulate in RAM unless `stream=True` is passed, causing potential out-of-memory\n",
      "errors for large sources or long-running streams and videos. See https://docs.ultralytics.com/modes/predict/ for help.\n",
      "\n",
      "Example:\n",
      "    results = model(source=..., stream=True)  # generator of Results objects\n",
      "    for r in results:\n",
      "        boxes = r.boxes  # Boxes object for bbox outputs\n",
      "        masks = r.masks  # Masks object for segment masks outputs\n",
      "        probs = r.probs  # Class probabilities for classification outputs\n",
      "\n",
      "WARNING ⚠️ imgsz=[1080] must be multiple of max stride 32, updating to [1088]\n",
      "\n",
      "WARNING ⚠️ inference results will accumulate in RAM unless `stream=True` is passed, causing potential out-of-memory\n",
      "errors for large sources or long-running streams and videos. See https://docs.ultralytics.com/modes/predict/ for help.\n",
      "\n",
      "Example:\n",
      "    results = model(source=..., stream=True)  # generator of Results objects\n",
      "    for r in results:\n",
      "        boxes = r.boxes  # Boxes object for bbox outputs\n",
      "        masks = r.masks  # Masks object for segment masks outputs\n",
      "        probs = r.probs  # Class probabilities for classification outputs\n",
      "\n",
      "WARNING ⚠️ imgsz=[1080] must be multiple of max stride 32, updating to [1088]\n",
      "\n",
      "WARNING ⚠️ inference results will accumulate in RAM unless `stream=True` is passed, causing potential out-of-memory\n",
      "errors for large sources or long-running streams and videos. See https://docs.ultralytics.com/modes/predict/ for help.\n",
      "\n",
      "Example:\n",
      "    results = model(source=..., stream=True)  # generator of Results objects\n",
      "    for r in results:\n",
      "        boxes = r.boxes  # Boxes object for bbox outputs\n",
      "        masks = r.masks  # Masks object for segment masks outputs\n",
      "        probs = r.probs  # Class probabilities for classification outputs\n",
      "\n",
      "WARNING ⚠️ imgsz=[1080] must be multiple of max stride 32, updating to [1088]\n",
      "\n",
      "WARNING ⚠️ inference results will accumulate in RAM unless `stream=True` is passed, causing potential out-of-memory\n",
      "errors for large sources or long-running streams and videos. See https://docs.ultralytics.com/modes/predict/ for help.\n",
      "\n",
      "Example:\n",
      "    results = model(source=..., stream=True)  # generator of Results objects\n",
      "    for r in results:\n",
      "        boxes = r.boxes  # Boxes object for bbox outputs\n",
      "        masks = r.masks  # Masks object for segment masks outputs\n",
      "        probs = r.probs  # Class probabilities for classification outputs\n",
      "\n",
      "WARNING ⚠️ imgsz=[1080] must be multiple of max stride 32, updating to [1088]\n",
      "\n",
      "WARNING ⚠️ inference results will accumulate in RAM unless `stream=True` is passed, causing potential out-of-memory\n",
      "errors for large sources or long-running streams and videos. See https://docs.ultralytics.com/modes/predict/ for help.\n",
      "\n",
      "Example:\n",
      "    results = model(source=..., stream=True)  # generator of Results objects\n",
      "    for r in results:\n",
      "        boxes = r.boxes  # Boxes object for bbox outputs\n",
      "        masks = r.masks  # Masks object for segment masks outputs\n",
      "        probs = r.probs  # Class probabilities for classification outputs\n",
      "\n",
      "WARNING ⚠️ imgsz=[1080] must be multiple of max stride 32, updating to [1088]\n",
      "\n",
      "WARNING ⚠️ inference results will accumulate in RAM unless `stream=True` is passed, causing potential out-of-memory\n",
      "errors for large sources or long-running streams and videos. See https://docs.ultralytics.com/modes/predict/ for help.\n",
      "\n",
      "Example:\n",
      "    results = model(source=..., stream=True)  # generator of Results objects\n",
      "    for r in results:\n",
      "        boxes = r.boxes  # Boxes object for bbox outputs\n",
      "        masks = r.masks  # Masks object for segment masks outputs\n",
      "        probs = r.probs  # Class probabilities for classification outputs\n",
      "\n",
      "WARNING ⚠️ imgsz=[1080] must be multiple of max stride 32, updating to [1088]\n",
      "\n",
      "WARNING ⚠️ inference results will accumulate in RAM unless `stream=True` is passed, causing potential out-of-memory\n",
      "errors for large sources or long-running streams and videos. See https://docs.ultralytics.com/modes/predict/ for help.\n",
      "\n",
      "Example:\n",
      "    results = model(source=..., stream=True)  # generator of Results objects\n",
      "    for r in results:\n",
      "        boxes = r.boxes  # Boxes object for bbox outputs\n",
      "        masks = r.masks  # Masks object for segment masks outputs\n",
      "        probs = r.probs  # Class probabilities for classification outputs\n",
      "\n",
      "WARNING ⚠️ imgsz=[1080] must be multiple of max stride 32, updating to [1088]\n",
      "\n",
      "WARNING ⚠️ inference results will accumulate in RAM unless `stream=True` is passed, causing potential out-of-memory\n",
      "errors for large sources or long-running streams and videos. See https://docs.ultralytics.com/modes/predict/ for help.\n",
      "\n",
      "Example:\n",
      "    results = model(source=..., stream=True)  # generator of Results objects\n",
      "    for r in results:\n",
      "        boxes = r.boxes  # Boxes object for bbox outputs\n",
      "        masks = r.masks  # Masks object for segment masks outputs\n",
      "        probs = r.probs  # Class probabilities for classification outputs\n",
      "\n",
      "WARNING ⚠️ imgsz=[1080] must be multiple of max stride 32, updating to [1088]\n",
      "\n",
      "WARNING ⚠️ inference results will accumulate in RAM unless `stream=True` is passed, causing potential out-of-memory\n",
      "errors for large sources or long-running streams and videos. See https://docs.ultralytics.com/modes/predict/ for help.\n",
      "\n",
      "Example:\n",
      "    results = model(source=..., stream=True)  # generator of Results objects\n",
      "    for r in results:\n",
      "        boxes = r.boxes  # Boxes object for bbox outputs\n",
      "        masks = r.masks  # Masks object for segment masks outputs\n",
      "        probs = r.probs  # Class probabilities for classification outputs\n",
      "\n",
      "WARNING ⚠️ imgsz=[1080] must be multiple of max stride 32, updating to [1088]\n",
      "\n",
      "WARNING ⚠️ inference results will accumulate in RAM unless `stream=True` is passed, causing potential out-of-memory\n",
      "errors for large sources or long-running streams and videos. See https://docs.ultralytics.com/modes/predict/ for help.\n",
      "\n",
      "Example:\n",
      "    results = model(source=..., stream=True)  # generator of Results objects\n",
      "    for r in results:\n",
      "        boxes = r.boxes  # Boxes object for bbox outputs\n",
      "        masks = r.masks  # Masks object for segment masks outputs\n",
      "        probs = r.probs  # Class probabilities for classification outputs\n",
      "\n",
      "WARNING ⚠️ imgsz=[1080] must be multiple of max stride 32, updating to [1088]\n",
      "\n",
      "WARNING ⚠️ inference results will accumulate in RAM unless `stream=True` is passed, causing potential out-of-memory\n",
      "errors for large sources or long-running streams and videos. See https://docs.ultralytics.com/modes/predict/ for help.\n",
      "\n",
      "Example:\n",
      "    results = model(source=..., stream=True)  # generator of Results objects\n",
      "    for r in results:\n",
      "        boxes = r.boxes  # Boxes object for bbox outputs\n",
      "        masks = r.masks  # Masks object for segment masks outputs\n",
      "        probs = r.probs  # Class probabilities for classification outputs\n",
      "\n",
      "WARNING ⚠️ imgsz=[1080] must be multiple of max stride 32, updating to [1088]\n",
      "\n",
      "WARNING ⚠️ inference results will accumulate in RAM unless `stream=True` is passed, causing potential out-of-memory\n",
      "errors for large sources or long-running streams and videos. See https://docs.ultralytics.com/modes/predict/ for help.\n",
      "\n",
      "Example:\n",
      "    results = model(source=..., stream=True)  # generator of Results objects\n",
      "    for r in results:\n",
      "        boxes = r.boxes  # Boxes object for bbox outputs\n",
      "        masks = r.masks  # Masks object for segment masks outputs\n",
      "        probs = r.probs  # Class probabilities for classification outputs\n",
      "\n",
      "WARNING ⚠️ imgsz=[1080] must be multiple of max stride 32, updating to [1088]\n",
      "\n",
      "WARNING ⚠️ inference results will accumulate in RAM unless `stream=True` is passed, causing potential out-of-memory\n",
      "errors for large sources or long-running streams and videos. See https://docs.ultralytics.com/modes/predict/ for help.\n",
      "\n",
      "Example:\n",
      "    results = model(source=..., stream=True)  # generator of Results objects\n",
      "    for r in results:\n",
      "        boxes = r.boxes  # Boxes object for bbox outputs\n",
      "        masks = r.masks  # Masks object for segment masks outputs\n",
      "        probs = r.probs  # Class probabilities for classification outputs\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from ultralytics import YOLO\n",
    "model = YOLO(\"runs/detect/train/weights/best.pt\")\n",
    "import glob\n",
    "\n",
    "for path in glob.glob('测试集/*.mp4'):\n",
    "    submit_json = []\n",
    "    results = model(path, conf=0.05, imgsz=1080,  verbose=False)\n",
    "    for idx, result in enumerate(results):\n",
    "        boxes = result.boxes  # Boxes object for bounding box outputs\n",
    "        masks = result.masks  # Masks object for segmentation masks outputs\n",
    "        keypoints = result.keypoints  # Keypoints object for pose outputs\n",
    "        probs = result.probs  # Probs object for classification outputs\n",
    "        obb = result.obb  # Oriented boxes object for OBB outputs\n",
    "\n",
    "        if len(boxes.cls) == 0:\n",
    "            continue\n",
    "        \n",
    "        xywh = boxes.xyxy.data.cpu().numpy().round()\n",
    "        cls = boxes.cls.data.cpu().numpy().round()\n",
    "        conf = boxes.conf.data.cpu().numpy()\n",
    "        for i, (ci, xy, confi) in enumerate(zip(cls, xywh, conf)):\n",
    "            submit_json.append(\n",
    "                {\n",
    "                    'frame_id': idx,\n",
    "                    'event_id': i+1,\n",
    "                    'category': category_labels[int(ci)],\n",
    "                    'bbox': list([int(x) for x in xy]),\n",
    "                    \"confidence\": float(confi)\n",
    "                }\n",
    "            )\n",
    "\n",
    "    with open('./result/' + path.split('/')[-1][:-4] + '.json', 'w', encoding='utf-8') as up:\n",
    "        json.dump(submit_json, up, indent=4, ensure_ascii=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "ef91df65-ed29-417f-b433-4d2fecc3e785",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "rm: cannot remove 'result.zip': No such file or directory\n",
      "  adding: result/ (stored 0%)\n",
      "  adding: result/test_6.json (deflated 94%)\n",
      "  adding: result/test_38.json (deflated 92%)\n",
      "  adding: result/test_14.json (deflated 88%)\n",
      "  adding: result/test_5.json (stored 0%)\n",
      "  adding: result/test_33.json (deflated 88%)\n",
      "  adding: result/test_13.json (stored 0%)\n",
      "  adding: result/test_7.json (deflated 43%)\n",
      "  adding: result/test_2.json (stored 0%)\n",
      "  adding: result/test_17.json (deflated 90%)\n",
      "  adding: result/test_8.json (stored 0%)\n",
      "  adding: result/test_28.json (deflated 90%)\n",
      "  adding: result/test_4.json (deflated 83%)\n",
      "  adding: result/test_24.json (deflated 92%)\n",
      "  adding: result/test_36.json (deflated 44%)\n",
      "  adding: result/test_35.json (deflated 89%)\n",
      "  adding: result/test_27.json (deflated 90%)\n",
      "  adding: result/test_10.json (stored 0%)\n",
      "  adding: result/test_32.json (deflated 92%)\n",
      "  adding: result/test_19.json (stored 0%)\n",
      "  adding: result/test_15.json (stored 0%)\n",
      "  adding: result/test_16.json (deflated 88%)\n",
      "  adding: result/test_34.json (deflated 91%)\n",
      "  adding: result/test_30.json (deflated 66%)\n",
      "  adding: result/test_29.json (deflated 86%)\n",
      "  adding: result/test_11.json (stored 0%)\n",
      "  adding: result/test_9.json (deflated 87%)\n",
      "  adding: result/test_12.json (stored 0%)\n",
      "  adding: result/test_1.json (deflated 92%)\n",
      "  adding: result/test_18.json (deflated 92%)\n",
      "  adding: result/test_25.json (deflated 64%)\n",
      "  adding: result/test_31.json (deflated 90%)\n",
      "  adding: result/test_20.json (stored 0%)\n",
      "  adding: result/test_23.json (deflated 91%)\n",
      "  adding: result/test_26.json (deflated 89%)\n",
      "  adding: result/test_3.json (deflated 88%)\n",
      "  adding: result/test_21.json (stored 0%)\n",
      "  adding: result/test_22.json (deflated 91%)\n",
      "  adding: result/test_39.json (stored 0%)\n",
      "  adding: result/test_37.json (deflated 43%)\n"
     ]
    }
   ],
   "source": [
    "!\\rm result/.ipynb_checkpoints/ -rf\n",
    "!\\rm result.zip\n",
    "!zip -r result.zip result/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ebbb24b8-5880-4e4e-9595-a1dfc64edfb4",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
